Optimising Parking Accessibility Near Healthcare and Educational Facilities in Melbourne

As an urban planner in the city of Melbourne, I want to identify areas near healthcare and educational facilities where parking is limited but demand is high, So that I can make informed decisions about improving parking infrastructure and accessibility for patients, students, and staff.

At the end of this use case you will:

• Learn how to import and combine data from various sources.
• Understand relationship between parking demand, pedestrian traffic and public facilities.
• Gain skills in analysing data and presenting data insights for clients

In growing cities like Melbourne, healthcare and educational facilities are essential parts of the community and attract high numbers of daily visitors. These places often have a lot of foot traffic, especially during rush hours. However, there is not always enough parking nearby for patients, students, and staff. This can cause inconvinience, delays, and access issues, especially for patients, students, and people who need these services.

This use case aims to explore parking accessibility around healthcare and educational institutions in Melbourne. By analysing data on car park capacity, facility locations, and pedestrian movement, the project seeks to identify areas with high demand but limited parking. The results will support urban planners in making informed decisions to improve parking infrastructure and future city planning.

• Off-street car parks with capacity and type
  Data collected as part of the City of Melbourne's Census of Land Use and Employment (CLUE). The data covers the period 2002-2023.
• Landmarks and places of interest, including schools, theatres, health services, sports facilities, places of worship, galleries and museums
  Dataset contains a description and co-ordinates of places of interest within the City of Melbourne.
• Pedestrian Counting System (counts per hour)
  This dataset contains hourly pedestrian counts since 2009 from pedestrian sensor devices located across the city. The data is updated on a monthly basis and can be used to determine variations in pedestrian activity throughout the day.
• Bus stops
  This data set shows the locations of the bus stops within the city of Melbourne, As the city of Melbourne do not run the bus services, this is simply to show the locations of the stops, this data does not include the services that run from each of the stops.
• Tram Tracks
  This dataset is a shapefile that contains all of the tram tracks within the city of Melbourne, this data set is generally used for City of Melbourne related queries, therefor we do not hold the entire tram network

Data Integration

Linking landmarks with nearby public parking

We combined data from landmarks (such as hospitals and schools) with nearby public parking locations. The distance between each landmark and every car park was calculated using their geographic coordinates. If a car park was located within 400 metres of a landmark, it was counted as nearby, and its number of parking spaces was included in the total. We also recorded the building addresses of these car parks to provide more location context.

Linking landmarks with pedestrian data

Each landmark was matched with pedestrian sensors located within a 400-metre radius. For each location, we calculated the average number of pedestrians detected nearby using 2024 sensor data. This provides an indication of foot traffic volume around that site. If no sensor was found near a landmark, the average pedestrian count was set to zero.

Combining parking and pedestrian data for each landmark

The parking and pedestrian datasets were merged using the landmark’s feature_name as a common key. This created a single summary table that includes both the total nearby parking spaces and the average pedestrian activity for each landmark. This integrated view helps assess demand and accessibility at each location.

Check for shared or overlapping car parks

We analysed whether certain public car parks were counted for multiple landmarks. This helps identify car parks that may serve more than one nearby facility, which is important when considering shared resources or potential congestion.

We found that some car parks are used by several landmarks:

• The car park at Royal Women’s Hospital (18–22 Flemington Road, Parkville) is the most shared one, connected to 6 different places nearby.
• Other car parks, like those on Grattan Street and Berkeley Street, are also used by 4 or 5 landmarks.
• Many shared car parks are found in areas like Carlton, East Melbourne, and North Melbourne.

Combining public transport data for each landmark

The nearby bus stops and tram tracks were calculated based on their distance to each landmark. Landmarks within 400 metres of bus stops or tram tracks were counted and stored as nearby_bus_stops and nearby_tram_tracks. These results were then merged into the landmark summary table.

This helps provide a more complete view of each location’s public transport accessibility, which may influence parking demand and ease of access.

Shared parking space adjustment

We adjust the number of public parking spaces available to each hospital or school when those spaces are shared with other nearby locations. It starts by listing every combination of landmarks and the car parks near them. Then, it counts how many landmarks are linked to each parking area. Instead of giving each landmark the full parking count, it divides the total equally among the ones sharing it. Finally, it adds up the adjusted numbers for each landmark, giving a more accurate view of how much parking is truly available to them. This helps avoid double-counting and supports better planning for areas where parking is shared.

Parking Demand Analysis

Parking Demand Risk Classification

We calculated the parking demand risk by comparing the number of available parking spaces to the number of pedestrians during peak hours. A new value, parking_per_peak_person, was calculated to represent how many parking spaces are available for each person during the busiest time. Based on this ratio, we classified each landmark into demand risk levels: High, Moderate, or Low. This helps identify locations where parking may be insufficient during peak times and supports decision-making for parking improvements.

Pedestrian Demand vs Parking Capacity Analysis

To create these visualisations, we wrote code that automatically generated hourly charts for all landmarks with available pedestrian sensor data. First, we filtered the landmarks to only include those with sensor data (peak_pedestrian_count > 0). Then, for each landmark, we identified nearby pedestrian sensors within 400 metres and calculated the average pedestrian count for each hour of the day. Next, we retrieved the adjusted parking spaces for each landmark and plotted this as a red dashed line across the chart to indicate parking capacity. The hourly pedestrian counts were plotted as blue lines with dots, showing how foot traffic changes throughout the day. Finally, we used subplots to display multiple landmarks side by side, making it easy to compare pedestrian demand and parking supply across different locations. This automated approach allows us to efficiently visualise and assess parking adequacy for all monitored landmarks.

By plotting pedestrian activity throughout the day against the available parking spaces, we can observe how demand fluctuates and whether parking supply is sufficient during busy times.

Key Insights:

• Some landmarks, like Carlton Gardens Primary School and Carlton Primary School, showed peak pedestrian counts that approached or exceeded their parking availability during the day. This indicates potential high demand or risk of parking shortages at certain peak times.

• Major universities such as RMIT University and University of Melbourne (VCA and Music) experienced very high pedestrian flows, yet still maintained parking supply above the peak pedestrian counts, suggesting relatively good parking accessibility.

• Hospitals (e.g., Royal Victorian Eye & Ear Hospital and Royal Dental Hospital) also had times where pedestrian volumes increased notably, but most remained within the parking capacity limits.

• State Library Victoria, although not a healthcare or education institution, showed steady foot traffic throughout the day but still within the parking capacity.

In summary, most landmarks generally have enough parking capacity during peak pedestrian times. However, smaller facilities and some schools may face challenges during peak hours. These areas may require further monitoring or parking management solutions.

Data visualisation

Interactive Map of landmark accessibility

This interactive map visualises key public landmarks in Melbourne—such as hospitals, schools, and community facilities—alongside parking and pedestrian activity data.

What the Map Shows:

1. City Centered View
   The map is centered on Melbourne’s CBD using its geographic coordinates (latitude: -37.8136, longitude: 144.9631) for a clear view of the city.

2. Landmark Markers
   Each landmark is marked with a colored icon:

   • Blue marker: There is pedestrian sensor data available for this location.
   • Gray marker: No pedestrian data is available.

   Clicking on any marker shows key details:

   • Name of the location
   • Type (e.g., hospital, school)
   • Number of nearby public car parks
   • Estimated parking space availability
   • Average pedestrian count (if available)
   • Peak pedestrian hour (if available)

3. Sensor Locations
   Green circles show the locations of pedestrian sensors, helping to visualise where foot traffic is being monitored.

This map shows important public landmarks in Melbourne,like hospitals, schools, and libraries along with useful information about parking and pedestrian activity nearby. Each blue marker represents one of these landmarks. When you click on a marker, a box pops up with these details, which are type, nearby public car parks, total parking spaces, avg pedestrian count, and peak hour.

Parking Demand and Public Transport Access in Melbourne

This interactive map gives a clearer picture of how busy certain areas are and how well they are supported by public transport. It focuses on landmarks with valid pedestrian sensor data, and it also shows the locations of bus and tram stops.

What This Map Shows

1. Landmarks with Parking Demand Risk
   • Each landmark is marked with a colored icon based on parking demand risk:
     • Red = High demand (likely parking shortage)
     • Orange = Moderate demand
     • Green = Low demand
     • Gray = Unknown or missing data
   • Clicking a marker shows:
     • Name and type of the place (e.g., hospital, university)
     • Peak pedestrian count (how many people walk past at the busiest time)
     • Estimated available parking spaces
     • Risk level for parking demand
2. Bus and Tram Stops
   • Orange dots show bus stops near landmarks.
   • Purple dots show tram stops nearby.
   • These help assess if public transport is available as an alternative to car parking.
3. Custom Legend
   • A built-in legend explains all the marker colors clearly, helping users interpret the map without needing extra guidance.

Key insight

• Helps identify which places are struggling with parking due to high pedestrian activity.
• Shows whether public transport options are nearby, offering possible solutions to reduce car usage.
• Supports planning decisions for infrastructure improvements, policy changes, or encouraging alternative transport modes.

This map turns complex urban data into an easy-to-understand, visual story that can help shape smarter transport and planning strategies in Melbourne.

Parking demand summary

This code groups landmarks based on their parking demand risk level (High, Moderate, or Low) and list the names of landmarks under each category. It helps present the data in a simple, structured format that makes it easy for stakeholders to quickly understand which locations face the highest pressure for parking and where improvements might be needed.

Public Transport Access Near High Parking Demand Landmarks

This analysis identifies public landmarks in Melbourne that have high parking demand and checks whether they are well-served by public transport.

What this step does:

• Filters for landmarks classified as having High Parking Demand Risk.
• For each of these landmarks:
  • Finds all bus stops within 400 meters.
  • Finds all tram stops within 400 meters.
• Prints a summary that includes:
  • Landmark name
  • Number of nearby bus stops
  • Number of nearby tram stops

Key insight:

By checking how many public transport options are close to high-demand locations, we can:

• Identify areas that lack alternative transport and may need more parking or better transit access.
• Support urban planning decisions that reduce car dependency by improving bus/tram infrastructure.
• Make data-driven recommendations to improve accessibility and reduce parking stress.

This helps ensure that busy landmarks are not just car-dependent but have practical and sustainable travel alternatives.

Conclusion

The parking demand analysis grouped healthcare and educational landmarks into three risk levels—high, moderate, and low—based on the ratio between adjusted parking spaces and peak pedestrian activity. Carlton Primary School and Kensington Primary School were classified as high-risk areas, indicating a potential parking shortage during peak times. These sites would benefit from either expanding public parking or encouraging the use of nearby public transportation to reduce car dependency.

Landmarks like The Royal Victorian Eye & Ear Hospital, Kangan Batman TAFE, and University of Melbourne (VCA and Music) fell into the moderate-risk category. While their parking conditions are not critical, these areas could face issues as demand increases. Measures such as promoting alternative transport options or monitoring peak usage patterns could help prevent future shortages.

In contrast, Carlton Gardens Primary School, RMIT University, University of Melbourne, and Royal Dental Hospital were assessed as low risk. These locations appear to have adequate parking relative to current pedestrian activity. No immediate interventions are necessary, but ongoing monitoring is recommended to ensure demand remains balanced with supply.

Public transport access plays a crucial role in supporting parking demand management. The analysis showed that high-risk landmarks vary significantly in their access to nearby bus and tram stops. For instance, Carlton Primary School is well-connected to both tram and bus networks, while Kensington Primary School has limited access. Strengthening public transport options near high-demand areas can provide effective alternatives to driving, easing pressure on parking infrastructure and enhancing overall accessibility.

Recommendations

• Enhance Public Transport Access:
  For high-risk areas with limited public transport (e.g., Kensington Primary School), consider increasing bus service frequency or evaluating the feasibility of new tram stops to reduce dependence on car travel.

• Promote Shared and Flexible Parking:
  Where parking spaces are shared by multiple landmarks, implement coordinated access policies and better signage to make the most efficient use of space.

• Prioritise Pedestrian Data Expansion:
  Install more pedestrian sensors near healthcare and educational hubs to improve demand forecasting and support data-driven planning.

• Dynamic Parking Management:
  Use peak-hour pedestrian patterns to guide pricing strategies, peak-time parking restrictions, or permit zones to balance usage.

• Encourage Active and Public Transport Use:
  Provide safe walking and cycling paths from public transport nodes to high-demand destinations to support mode shift.

These insights and actions aim to promote a balanced, equitable, and sustainable approach to urban parking management in Melbourne.